{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K近邻算法(K-Nearest-Neighbor)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN算法步骤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 计算测试数据距离每个样本点的距离。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 将计算得到的距离进行排序。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 选出距离较近的K个样本，并对其类别进行分类。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 距离测试数据距离最近的样本的类别就是测试数据所属的类别。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 约会网站的配对效果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 项目概述"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 海伦使用约会网站寻找约会对象，经过一段时间发现，曾经交往过三种类型的人：\n",
    "  * 1.不喜欢的人\n",
    "  * 2.魅力一般的人\n",
    "  * 3.极具魅力的人\n",
    "* 她希望：\n",
    "  * 1.工作日与魅力一般的人约会\n",
    "  * 2.周末与极具魅力的人约会\n",
    "  * 3.不喜欢的人直接排除"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 收集数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 数据集在datingTestSet2.txt中\n",
    "* 前3个属性分别是：玩游戏所耗时间比，飞行里程数，消费冰淇淋数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 准备数据：使用 Python 解析文本文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入包\n",
    "import numpy as np\n",
    "import operator#运算符模块\n",
    "from os import listdir\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将文本记录转换为numpy的解析程序\n",
    "def file2matrix(filename):\n",
    "    fr = open(filename)\n",
    "    numberlines = len(fr.readlines())#获取行数\n",
    "    returnMat = np.zeros((numberlines,3))#空矩阵\n",
    "    classlabel = []#label\n",
    "    fr = open(filename)\n",
    "    index = 0\n",
    "    for line in fr.readlines():\n",
    "        line = line.strip()#移除头尾的字符\n",
    "        listFromline = line.split('\\t')#以'\\t'切分\n",
    "        returnMat[index,:] = listFromline[0:3]#获取每行的数据保存在returnMat中\n",
    "        classlabel.append(int(listFromline[-1]))#将每行的标签保存在classlabel中\n",
    "        index+=1\n",
    "    return returnMat,classlabel#返回每个样本的属性和标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析数据：使用 Matplotlib 画二维散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')\n",
    "fig = plt.figure()#创建一个窗口\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(datingDataMat[:, 0], datingDataMat[:, 1], 15.0*np.array(datingLabels), 15.0*np.array(datingLabels))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 这是三维空间图，将其投影到X轴就会得到上面的二维图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\mpl_toolkits\\mplot3d\\art3d.py:726: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if zdir == 'x':\n",
      "D:\\Anaconda\\lib\\site-packages\\mpl_toolkits\\mplot3d\\art3d.py:728: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  elif zdir == 'y':\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D#导入包\n",
    "\n",
    "datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')\n",
    "ax = plt.subplot(111,projection='3d')#创建一个三维的绘图工程\n",
    "x1 = datingDataMat[:,0]\n",
    "x2 = datingDataMat[:,1]\n",
    "x3 = datingDataMat[:,2]\n",
    "ax.scatter(x1,x2,15.0*np.array(datingLabels), 15.0*np.array(datingLabels),c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 归一化数据，目的是使数据的大小在0-1的范围内。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据归一化\n",
    "#公式：y = (X - Xmin) / (Xmax - Xmin)\n",
    "def autoNorm(dataSet):\n",
    "    #计算最大值和最小值\n",
    "    minval = dataSet.min(0)\n",
    "    maxval = dataSet.max(0)\n",
    "    n = dataSet.shape\n",
    "    normDataSet = np.zeros(n)\n",
    "    m = dataSet.shape[0]\n",
    "    ranges = maxval - minval\n",
    "    #每个属性与最小值之差的矩阵\n",
    "    normDataSet = dataSet - np.tile(minval,(m,1))\n",
    "    #最小值之差除以最大值减最小值\n",
    "    normDataSet = normDataSet / np.tile(ranges,(m,1))\n",
    "    return normDataSet, ranges, minval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "#计算距离\n",
    "def classify0(inX, dataSet, labels, k):\n",
    "    dataSetSize = dataSet.shape[0]\n",
    "    #距离度量 度量公式为欧氏距离\n",
    "    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet\n",
    "    sqDiffMat = diffMat**2\n",
    "    sqDistances = sqDiffMat.sum(axis=1)\n",
    "    distances = sqDistances**0.5\n",
    "    \n",
    "    #将距离排序：从小到大\n",
    "    sortedDistIndicies = distances.argsort()\n",
    "    #选取前K个最短距离， 选取这K个中最多的分类类别\n",
    "    classCount={}\n",
    "    for i in range(k):\n",
    "        voteIlabel = labels[sortedDistIndicies[i]]\n",
    "        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 \n",
    "    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    return sortedClassCount[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "#约会网站的测试\n",
    "def datingClassTest():\n",
    "    \"\"\"\n",
    "    Desc:\n",
    "        对约会网站的测试方法\n",
    "    parameters:\n",
    "        none\n",
    "    return:\n",
    "        错误数\n",
    "    \"\"\"\n",
    "    # 设置测试数据的的一个比例（训练数据集比例=1-hoRatio）\n",
    "    hoRatio = 0.1  # 测试范围,一部分测试一部分作为样本\n",
    "    # 从文件中加载数据\n",
    "    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')  # load data setfrom file\n",
    "    # 归一化数据\n",
    "    normMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "    # m 表示数据的行数，即矩阵的第一维\n",
    "    m = normMat.shape[0]\n",
    "    # 设置测试的样本数量， numTestVecs:m表示训练样本的数量\n",
    "    numTestVecs = int(m * hoRatio)\n",
    "    print ('numTestVecs=', numTestVecs)\n",
    "    errorCount = 0.0\n",
    "    for i in range(numTestVecs):\n",
    "        # 对数据测试\n",
    "        classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 6)\n",
    "        print (\"the classifier came back with: %d, the real answer is: %d\" % (classifierResult, datingLabels[i]))\n",
    "        if (classifierResult != datingLabels[i]): \n",
    "            errorCount += 1.0\n",
    "            print('error classify')\n",
    "    print (\"the total error rate is: %f\" % (errorCount / float(numTestVecs)))\n",
    "    print (errorCount)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用KNN进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classifyPerson():\n",
    "    resultList = ['不喜欢','魅力一般','极具魅力']#标签\n",
    "    #输入3个属性值\n",
    "    game = float(input('play games?'))\n",
    "    fly = float(input('fly distance?'))\n",
    "    icecream = float(input('ice cream?'))\n",
    "    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')\n",
    "    normMat,ranges, minval = autoNorm(datingDataMat)\n",
    "    inArr = np.array([game,fly,icecream])\n",
    "    classifierResult = classify0((inArr - minval) / ranges,normMat,datingLabels,3)\n",
    "    print('You will probably like:',resultList[classifierResult - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if __name__ == '__main__':\n",
    "#     classifyPerson()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numTestVecs= 100\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 2\n",
      "error classify\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 3\n",
      "error classify\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "error classify\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 3\n",
      "error classify\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 1\n",
      "error classify\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the total error rate is: 0.050000\n",
      "5.0\n"
     ]
    }
   ],
   "source": [
    "datingClassTest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
